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1 de oct. de 2019 · In this paper, we mainly focus on the short-term air traffic flow prediction, in which the model predicts the air traffic flow in the near future, typically 0 to 30 minutes [3]. Since air traffic is a complicated time-varying system, any current traffic state may have significant influence on future traffic flow, that is why air traffic flow prediction is important to the ATC research.
16 de sept. de 2021 · Deep neural networks are being increasingly used for short-term traffic flow prediction, which can be generally categorized as CNNs or GNNs. CNNs typically partition an underlying territory into ...
A project for traffic flow prediction. Contribute to bobbychovip/DeepTraffic development by creating an account on GitHub.
1 de dic. de 2019 · Data-driven models collect historical traffic flow data from various sources (sensors, GPS etc.) and utilize this collected data to develop a prediction. Traffic flow prediction also provides realistic travel time estimates to the users. However, the traffic flow data is periodic and its pattern can differ between working days and weekends.
As a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the influence of external factors. Effective long-term traffic flow prediction has become a challenging ...
14 de mar. de 2020 · The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom.
1 de mar. de 2024 · Problem 1 Traffic Flow Prediction. Based on continuous historical traffic data, the goal of traffic flow prediction is to estimate the number of vehicles on each road or region in the next time interval Δ t. As a result, I = I 0, I 1, I 2, …, I n denotes historical observations of traffic flow, and the goal is to predict I n + 1.
